class_docstr: Hand-coded policy that returns random actions.
class_docstr_tok: ["Hand-coded","policy","that","returns","random","actions","."]
code: def _get_dummy_batch_from_view_requirements(
      self, batch_size: int = 1
  ):
      return SampleBatch(
          {
              SampleBatch.OBS: tree.map_structure(
                  lambda s: s[None], self.observation_space.sample()
              ),
          }
      )
  
file_path: ray/python/ray/rllib/examples/policy/random_policy.py
imports: from gymnasium.spaces import Box
  import numpy
  import random
  import tree
  from typing import List, Optional, Union
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.typing import ModelWeights, TensorStructType, TensorType
label_desc: Use image node_id 8 for calling the RandomPolicy obj's underlying member method code with example usage: obj._get_dummy_batch_from_view_requirements(batch_size) and returns: SampleBatch